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Gaussian component mixtures and CAR models in Bayesian disease mapping

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  • Moraga, Paula
  • Lawson, Andrew B.

Abstract

Hierarchical Bayesian models involving conditional autoregression (CAR) components are commonly used in disease mapping. An alternative model to the proper or improper CAR is the Gaussian component mixture (GCM) model. A review of CAR and GCM models is provided in univariate settings where only one disease is considered, and also in multivariate situations where in addition to the spatial dependence between regions, the dependence among multiple diseases is analyzed. A performance comparison between models using a set of simulated data to help illustrate their respective properties is reported. The results show that both in univariate and multivariate settings, both models perform in a comparable way under a wide range of conditions. GCM and CAR models are applied for estimating the relative risk of low birth weight in Georgia, USA, in the year 2000.

Suggested Citation

  • Moraga, Paula & Lawson, Andrew B., 2012. "Gaussian component mixtures and CAR models in Bayesian disease mapping," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1417-1433.
    • Handle: RePEc:eee:csdana:v:56:y:2012:i:6:p:1417-1433
    DOI: 10.1016/j.csda.2011.11.011
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    References listed on IDEAS

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    1. Ian H. Langford & Alistair H. Leyland & Jon Rasbash & Harvey Goldstein, 1999. "Multilevel Modelling of the Geographical Distributions of Diseases," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 48(2), pages 253-268.
    2. Xiaoping Jin & Sudipto Banerjee & Bradley P. Carlin, 2007. "Order‐free co‐regionalized areal data models with application to multiple‐disease mapping," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(5), pages 817-838, November.
    3. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
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    Cited by:

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    2. Zirong Ye & Li Xu & Zi Zhou & Yafei Wu & Ya Fang, 2018. "Application of SCM with Bayesian B-Spline to Spatio-Temporal Analysis of Hypertension in China," IJERPH, MDPI, vol. 15(1), pages 1-18, January.
    3. Li Xu & Qingshan Jiang & David R. Lairson, 2019. "Spatio-Temporal Variation of Gender-Specific Hypertension Risk: Evidence from China," IJERPH, MDPI, vol. 16(22), pages 1-26, November.

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